1. Assessment of Climate Change Impact on Eastern Washington Agriculture Claudio O. Stöckle Biological Systems Engineering, Washington State University USA

2. Objective   Assess the potential impact of climate change and elevated atmospheric CO 2 concentration on selected crops in eastern Washington, region that produces most of the state’s agricultural output value.

4. Correlation between CO 2 Concentrations and Temperature The current concentration is the highest in 800,000 years, as determined by ice core data a The 800,000-year records of atmospheric carbon dioxide (red; parts per million, p.p.m.) and methane (green; parts per billion, p.p.b.) from the EPICA Dome C ice core together with a temperature reconstruction (relative to the average of the past millennium) based on the deuterium– hydrogen ratio of the ice, reinforce the tight coupling between greenhouse-gas concentrations and climate observed in previous, shorter records. The 100,000-year ‘sawtooth’ variability undergoes a change about 450,000 years ago, with the amplitude of variation, especially in the carbon dioxide and temperature records, greater since that point than it was before. Concentrations of greenhouse gases in the modern atmosphere are highly anomalous with respect to natural greenhouse-gas variations (present-day concentrations are around 380 p.p.m. for carbon dioxide and 1,800 p.p.b. for methane). b The carbon dioxide and methane trends from the past 2,000 years. Ed Brook, Nature 453, 291 (2008).

5. Source: IPCC Global Greenhouse Gas Trends

6. General Circulation Models   Four GCMs were selected for this study: PCM1, CCSM3, ECHAM5, and CGCM3.  PCM1 projects less warming and CCSM3 more warming for eastern WA. The other two GCMs are intermediate.  The GCMs project an increase in precipitation (3 to 9% by 2020 and 2080, respectively) with some differences in distribution, and with a larger relative increase in the winter.

7. Annual Precipitation and Potential Evapotranspiration ETo (mm)

8. Seasonal (April 1- Sept 30) Precipitation and Potential Evapotranspiration ETo (mm)

9. Annual and Season Mean Temperature ( o C)

10. Sunnyside Annual 202020402080 CCSM31.92.83.6 PCM11.32.23.0 Sunnyside Seasonal 202020402080 CCSM31.82.93.8 PCM11.31.92.8 Annual Temperature Difference with Baseline ( o C)

11. Change in frost- free period (days)

12. Probability Distribution of Tmax (June/July)

13. Probability Distribution of Tmin (April)

14. Schlenker and Roberts (2008) National Bureau of Economic Research

15. Corn Schlenker and Roberts (2008) National Bureau of Economic Research

16. What about Atmospheric CO 2 increase? IPCC Projections

17. Relative change of Radiation-use efficiency for wheat and maize simulated with the CTP model (Stockle and Kemanian, 2009)

18. Free-Air CO 2 Enrichment (FACE) Experiments

19. Long et al. (2004) Annual Rev. Plant Biol. 55

21. Sour Orange Trees (13 years of data) Idso and Kimball BA (2001) Env Exp Bot 46

22. Assessment Approach   Relied on crop simulation modeling with interpretation based on literature and expert opinions.  CropSyst, a cropping systems model developed at WSU was used for the assessment.  Insect and disease models were used to complement the evaluation.

23. CropSyst has been tested and applied in all continents and under a wide range of climatic conditions ClimGen The WSU weather generator ClimGen was used to generate daily series of projected weather. CropSyst has been used for climate change assessment in studies elsewhere.

24. Assessment Approach   Focus on the major agricultural commodities in terms of economic value: apples, potatoes, and wheat.  Wheat is the dominant dryland crop.  Potato is the main irrigated annual crop.  Apple is the main irrigated tree fruit crop.

25. Assessment Approach   Daily weather data for the years 1975-2005 were used to establish a baseline for change.  Projections of daily precipitation and temperature from the four GCMs were used to define three climate change scenarios: 2020 (2010 – 2039) 2040 (2030 – 2059) 2080 (2070 – 2099)

26. Assessment Approach   The following locations (crops) were included in the analysis: o Pullman (winter and spring wheat, high precipitation) o Saint John (winter and spring wheat, intermediate precipitation) o Lind (winter wheat, low precipitation) o Othello (potatoes, irrigated) o Sunnyside (apples, irrigated)

28. Assessment Approach   Computer simulations of crop growth and yield assumed adequate supply of water and nutrients and good control of pests and diseases.  The only variables were climate change and CO 2 elevation.  The impact of possible irrigation water shortages was assessed in a complementary effort (hydrology sector).

29. Assessment Approach   The A1B IPCC emission scenario was used to project atmospheric CO 2 concentration.  Crop biomass productivity (a parameter that affects several simulated processes including crop water use) was assumed to increase 20% with a CO 2 change from 370 to 600 PPM (FACE experiments).

30. Results

31. Winter Wheat

32. Spring Wheat

33. Potatoes

34. Apples

35. Codling Moth

36. Average Number of Days of Powdery Mildew Risk

37. Conclusions   It is projected that the impact of climate change alone on selected but economically important crops in eastern WA would be generally mild in the short term (i.e., next couple of decades), but increasingly detrimental with time (potential yield losses reaching 25% for some crops by the end of the century).

38. Conclusions   However, the projected CO 2 increase is expected to provide significant mitigation to the effect of warming.  In fact, if the projected beneficial effect of CO 2 elevation are fully realized, some crops may obtain important yield gains.  Adaptation based on changes in management (e.g., planting dates) or on new research (e.g., better adapted varieties) can provide additional mitigation or further enhance CO 2 effects.

39. Conclusions   Caveats to consider: o Possible changes in the frequency and persistence of extreme temperature effects are not well represented in current climate projections o We have assumed good control of pests and diseases, but these could affect crops in ways not described here o Availability of irrigation water may become a significant limiting factor in some areas.

40. Conclusions   Caveats to consider: o Focus of the study is on yields, but quality can be affected even when yields increase. o The economic cost of adaptation (e.g., management for increased pest control or greater nitrogen fertilization requirements) should be accounted for in future studies.

41. Thank you.